The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

This paper describes the parallel implementation of fastDNAml, a program for the maximum likelihood inference of phylogenetic trees from DNA sequence data. Mathematical means of inferring phylogenetic trees have been made possible by the wealth of DNA data now available. Maximum likelihood analysis of phylogenetic trees is extremely computationally intensive. Availability of computer resources is a key factor limiting use of such analyses. fastDNAml is implemented in serial, PVM, and MPI versions, and may be modified to use other message passing libraries in the future. We have developed a viewer for comparing phylogenies. We tested the scaling behavior of fastDNAml on an IBM RS/6000 SP up to 64 processors. The parallel version of fastDNAml is one of very few computational phylogenetics codes that scale well. fastDNAml is available for download as source code or compiled for Linux or AIX.

We developed a parallel processing system for analyzing phylogenetic relationships of microorganisms based on a maximum likelihood method. Methods for inferring relationships from molecular sequence data are especially valuable, given the enormous increases in DNA sequence data. The maximum likelihood method uses concrete models of the evolutionary process and are well-motivated statistically, but the computational cost has hindered the use of this method for inferring trees with more than about 20 organisms. We parallelized the maximum likelihood method by utilizing two types of parallelism, parallel evaluation of phylogenetic trees and parallel computation of likelihood values. By combining these two parallelisms, we obtained significant speedup on the Intel Touchstone DELTA.

We have developed a new tool, called fastDNAml, for constructing phylogenetic trees from DNA sequences. The program can be run on a wide variety of computers ranging from Unix workstations to massively parallel systems, and is available from the Ribosomal Database Project (RDP) by anonymous FTP. Our program uses a maximum likelihood approach and is based on version 3.3 of Felsenstein's dnaml program. Several enhancements, including algorithmic changes, significantly improve performance and reduce memory usage, making it feasible to construct even very large trees. Trees containing 40-100 taxa have been easily generated, and phylogenetic estimates are possible even when hundreds of sequences exist. We are currently using the tool to construct a phylogenetic tree based on 473 small subunit rRNA sequences from prokaryotes.

This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential secondary and tertiary structural interactions, but also provides the covariation evidence that supports the proposed bondings, and any counterevidence that can be detected in the known sequences. A small number of previously unknown higher-order structural features have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. We are systematically studying mitochondrial rRNA, seeking tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. To test hypotheses suggested by an examination of our program's output, our colleagues in biology are sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosomal structure.